Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = '/data'
!pip install matplotlib==2.0.2
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Collecting matplotlib==2.0.2
  Downloading https://files.pythonhosted.org/packages/60/d4/6b6d8a7a6bc69a1602ab372f6fc6e88ef88a8a96398a1a25edbac636295b/matplotlib-2.0.2-cp36-cp36m-manylinux1_x86_64.whl (14.6MB)
    100% |████████████████████████████████| 14.6MB 45kB/s  eta 0:00:01
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib==2.0.2)
Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Requirement already satisfied: python-dateutil in /opt/conda/lib/python3.6/site-packages (from matplotlib==2.0.2)
Installing collected packages: matplotlib
  Found existing installation: matplotlib 2.1.0
    Uninstalling matplotlib-2.1.0:
      Successfully uninstalled matplotlib-2.1.0
Successfully installed matplotlib-2.0.2
You are using pip version 9.0.1, however version 18.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f2626e65320>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f2626d87780>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_height, image_width, image_channels), name = 'input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name = 'input_z')
    learning_rate = tf.placeholder(tf.float32, name = 'learning_rate')

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 497, in start\n    self.io_loop.start()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events\n    self._handle_recv()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback\n    callback(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2907, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2961, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-79af05ee5890>", line 23, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/workspace/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/workspace/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/workspace/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    with tf.variable_scope('discriminator', reuse = reuse):
        
        h1 = tf.layers.conv2d(images, 64, 5, strides = 2, padding = 'same')
        h1 = tf.maximum(0.2*h1, h1)
        
        h2 = tf.layers.conv2d(h1, 128, 5, strides = 1, padding ='same')
        h2 = tf.layers.batch_normalization(h2, training = True)
        h2 = tf.maximum(0.2*h2, h2)
        
        h3 = tf.layers.conv2d(h2, 256, 5, strides = 2, padding = 'same')
        h3 = tf.layers.batch_normalization(h3, training = True)
        h3 = tf.maximum(0.2*h3, h3)
        
        flatten = tf.reshape(h3, (-1, 7*7*256))
        logits = tf.layers.dense(flatten, 1)
        output = tf.sigmoid(logits)
        
        return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse= not is_train):    
        x1 = tf.layers.dense(z, 7*7*256, activation= None)
        
        h2 = tf.reshape(x1, (-1, 7, 7, 256))
        h2 = tf.layers.batch_normalization(h2, training = is_train)
        h2 = tf.maximum(0.2*h2, h2)
        
        h3 = tf.layers.conv2d_transpose(h2, 128, 5, strides = 1, padding = 'same')
        h3 = tf.layers.batch_normalization(h3, training = is_train)
        h3 = tf.maximum(0.2*h3, h3)
        
        h4 = tf.layers.conv2d_transpose(h3, 64, 5, strides = 2, padding = 'same')
        h4 = tf.layers.batch_normalization(h4, training = is_train)
        h4 = tf.maximum(0.2*h4, h4)
        
        logits = tf.layers.conv2d_transpose(h4, out_channel_dim, 5, strides=2, padding='same')
        
        output = tf.tanh(logits)
        return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    input_fake = generator(input_z, out_channel_dim, is_train = True)
    out_real, logits_real = discriminator(input_real)
    out_fake, logits_fake = discriminator(input_fake, reuse = True)

    generator_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits_fake, labels = tf.ones_like(out_fake)))

    loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits_real, labels = tf.ones_like(out_real)* (1 - 0.2)))
    loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits_fake, labels = tf.zeros_like(out_fake)))
    
    total_loss = loss_real + loss_fake

    return total_loss, generator_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): 
        
        d_train = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

        return d_train, g_train


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    input_real, input_fake, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    dloss, gloss = model_loss(input_real, input_fake, data_shape[3])
    d_train_opt, g_train_opt = model_opt(dloss, gloss, learning_rate, beta1)

    print_every = 10
    show_every = 5
    n_images = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                steps += 1
                batch_z = np.random.uniform(-1, 1, size= (batch_size, z_dim))
                batch_images = batch_images * 2.0
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_fake: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_fake: batch_z, lr: learning_rate})

                if steps % print_every == 0:
                    d_loss = dloss.eval({input_real: batch_images, input_fake: batch_z, lr: learning_rate})
                    g_loss = gloss.eval({input_fake: batch_z})
                    print("Epoch {}/{}....".format(epoch_i + 1, epoch_count),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}....".format(d_loss),
                          "Generator Loss: {:.4f}".format(g_loss))
                
                if steps % 100 == 0:
                    show_generator_output(sess, n_images, input_fake, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 100
learning_rate = 0.005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2.... Batch 10... Discriminator Loss: 4.6695.... Generator Loss: 0.5537
Epoch 1/2.... Batch 20... Discriminator Loss: 1.3937.... Generator Loss: 2.8597
Epoch 1/2.... Batch 30... Discriminator Loss: 3.2117.... Generator Loss: 0.8488
Epoch 1/2.... Batch 40... Discriminator Loss: 6.9610.... Generator Loss: 12.2152
Epoch 1/2.... Batch 50... Discriminator Loss: 1.4089.... Generator Loss: 11.1915
Epoch 1/2.... Batch 60... Discriminator Loss: 2.9467.... Generator Loss: 0.3446
Epoch 1/2.... Batch 70... Discriminator Loss: 2.3373.... Generator Loss: 0.4149
Epoch 1/2.... Batch 80... Discriminator Loss: 1.6979.... Generator Loss: 4.6057
Epoch 1/2.... Batch 90... Discriminator Loss: 5.4001.... Generator Loss: 7.5667
Epoch 1/2.... Batch 100... Discriminator Loss: 1.7144.... Generator Loss: 0.6453
Epoch 1/2.... Batch 110... Discriminator Loss: 1.8240.... Generator Loss: 1.4556
Epoch 1/2.... Batch 120... Discriminator Loss: 0.7905.... Generator Loss: 2.0754
Epoch 1/2.... Batch 130... Discriminator Loss: 1.8632.... Generator Loss: 0.8087
Epoch 1/2.... Batch 140... Discriminator Loss: 1.0494.... Generator Loss: 2.2529
Epoch 1/2.... Batch 150... Discriminator Loss: 0.8602.... Generator Loss: 5.6469
Epoch 1/2.... Batch 160... Discriminator Loss: 0.8204.... Generator Loss: 1.6550
Epoch 1/2.... Batch 170... Discriminator Loss: 0.9785.... Generator Loss: 1.4780
Epoch 1/2.... Batch 180... Discriminator Loss: 0.7471.... Generator Loss: 2.7401
Epoch 1/2.... Batch 190... Discriminator Loss: 0.8134.... Generator Loss: 1.8523
Epoch 1/2.... Batch 200... Discriminator Loss: 1.1127.... Generator Loss: 1.1894
Epoch 1/2.... Batch 210... Discriminator Loss: 0.8587.... Generator Loss: 1.9789
Epoch 1/2.... Batch 220... Discriminator Loss: 1.9409.... Generator Loss: 0.4244
Epoch 1/2.... Batch 230... Discriminator Loss: 1.8057.... Generator Loss: 0.5467
Epoch 1/2.... Batch 240... Discriminator Loss: 1.8010.... Generator Loss: 1.2770
Epoch 1/2.... Batch 250... Discriminator Loss: 0.8416.... Generator Loss: 2.1834
Epoch 1/2.... Batch 260... Discriminator Loss: 1.1348.... Generator Loss: 1.6562
Epoch 1/2.... Batch 270... Discriminator Loss: 1.6922.... Generator Loss: 2.7224
Epoch 1/2.... Batch 280... Discriminator Loss: 3.3899.... Generator Loss: 0.1528
Epoch 1/2.... Batch 290... Discriminator Loss: 1.0907.... Generator Loss: 1.2122
Epoch 1/2.... Batch 300... Discriminator Loss: 1.4252.... Generator Loss: 0.6338
Epoch 1/2.... Batch 310... Discriminator Loss: 1.9172.... Generator Loss: 0.5175
Epoch 1/2.... Batch 320... Discriminator Loss: 1.3971.... Generator Loss: 1.0194
Epoch 1/2.... Batch 330... Discriminator Loss: 1.5326.... Generator Loss: 0.8882
Epoch 1/2.... Batch 340... Discriminator Loss: 0.9937.... Generator Loss: 2.4795
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Epoch 2/2.... Batch 1130... Discriminator Loss: 0.7677.... Generator Loss: 1.7832
Epoch 2/2.... Batch 1140... Discriminator Loss: 1.2732.... Generator Loss: 0.8316
Epoch 2/2.... Batch 1150... Discriminator Loss: 0.9307.... Generator Loss: 1.2827
Epoch 2/2.... Batch 1160... Discriminator Loss: 1.5368.... Generator Loss: 0.6188
Epoch 2/2.... Batch 1170... Discriminator Loss: 0.8867.... Generator Loss: 2.6475
Epoch 2/2.... Batch 1180... Discriminator Loss: 1.4051.... Generator Loss: 0.7952
Epoch 2/2.... Batch 1190... Discriminator Loss: 0.8986.... Generator Loss: 1.3725
Epoch 2/2.... Batch 1200... Discriminator Loss: 0.9072.... Generator Loss: 2.6814
Epoch 2/2.... Batch 1210... Discriminator Loss: 0.9362.... Generator Loss: 1.2990
Epoch 2/2.... Batch 1220... Discriminator Loss: 1.0242.... Generator Loss: 1.1835
Epoch 2/2.... Batch 1230... Discriminator Loss: 0.8072.... Generator Loss: 2.0312
Epoch 2/2.... Batch 1240... Discriminator Loss: 0.8939.... Generator Loss: 1.4216
Epoch 2/2.... Batch 1250... Discriminator Loss: 1.0535.... Generator Loss: 2.7326
Epoch 2/2.... Batch 1260... Discriminator Loss: 0.9931.... Generator Loss: 1.3485
Epoch 2/2.... Batch 1270... Discriminator Loss: 0.8368.... Generator Loss: 1.5586
Epoch 2/2.... Batch 1280... Discriminator Loss: 1.0175.... Generator Loss: 1.9255
Epoch 2/2.... Batch 1290... Discriminator Loss: 0.9337.... Generator Loss: 1.3785
Epoch 2/2.... Batch 1300... Discriminator Loss: 0.8403.... Generator Loss: 1.7712
Epoch 2/2.... Batch 1310... Discriminator Loss: 1.2786.... Generator Loss: 0.8279
Epoch 2/2.... Batch 1320... Discriminator Loss: 0.9478.... Generator Loss: 1.3574
Epoch 2/2.... Batch 1330... Discriminator Loss: 0.8712.... Generator Loss: 1.7106
Epoch 2/2.... Batch 1340... Discriminator Loss: 0.9561.... Generator Loss: 1.2614
Epoch 2/2.... Batch 1350... Discriminator Loss: 0.8285.... Generator Loss: 1.7646
Epoch 2/2.... Batch 1360... Discriminator Loss: 1.5399.... Generator Loss: 0.5930
Epoch 2/2.... Batch 1370... Discriminator Loss: 0.9175.... Generator Loss: 2.0537
Epoch 2/2.... Batch 1380... Discriminator Loss: 0.9237.... Generator Loss: 2.3616
Epoch 2/2.... Batch 1390... Discriminator Loss: 1.1092.... Generator Loss: 1.0533
Epoch 2/2.... Batch 1400... Discriminator Loss: 1.2509.... Generator Loss: 0.7875
Epoch 2/2.... Batch 1410... Discriminator Loss: 0.8909.... Generator Loss: 1.5157
Epoch 2/2.... Batch 1420... Discriminator Loss: 0.9981.... Generator Loss: 2.5493
Epoch 2/2.... Batch 1430... Discriminator Loss: 0.8424.... Generator Loss: 1.7234
Epoch 2/2.... Batch 1440... Discriminator Loss: 0.8959.... Generator Loss: 1.8827
Epoch 2/2.... Batch 1450... Discriminator Loss: 1.0387.... Generator Loss: 2.3601
Epoch 2/2.... Batch 1460... Discriminator Loss: 1.5883.... Generator Loss: 0.6288
Epoch 2/2.... Batch 1470... Discriminator Loss: 1.1115.... Generator Loss: 1.1497
Epoch 2/2.... Batch 1480... Discriminator Loss: 0.8792.... Generator Loss: 1.4222
Epoch 2/2.... Batch 1490... Discriminator Loss: 1.2114.... Generator Loss: 0.8078
Epoch 2/2.... Batch 1500... Discriminator Loss: 1.0104.... Generator Loss: 1.2385
Epoch 2/2.... Batch 1510... Discriminator Loss: 1.2406.... Generator Loss: 0.9177
Epoch 2/2.... Batch 1520... Discriminator Loss: 0.7462.... Generator Loss: 1.9718
Epoch 2/2.... Batch 1530... Discriminator Loss: 1.0821.... Generator Loss: 1.0824
Epoch 2/2.... Batch 1540... Discriminator Loss: 1.6059.... Generator Loss: 0.6360
Epoch 2/2.... Batch 1550... Discriminator Loss: 0.9405.... Generator Loss: 1.3310
Epoch 2/2.... Batch 1560... Discriminator Loss: 1.3825.... Generator Loss: 0.6448
Epoch 2/2.... Batch 1570... Discriminator Loss: 1.2503.... Generator Loss: 0.9450
Epoch 2/2.... Batch 1580... Discriminator Loss: 1.0331.... Generator Loss: 1.0310
Epoch 2/2.... Batch 1590... Discriminator Loss: 1.5863.... Generator Loss: 0.5478
Epoch 2/2.... Batch 1600... Discriminator Loss: 1.0600.... Generator Loss: 1.1242
Epoch 2/2.... Batch 1610... Discriminator Loss: 1.0046.... Generator Loss: 1.2073
Epoch 2/2.... Batch 1620... Discriminator Loss: 1.0767.... Generator Loss: 1.1049
Epoch 2/2.... Batch 1630... Discriminator Loss: 0.8546.... Generator Loss: 2.1628
Epoch 2/2.... Batch 1640... Discriminator Loss: 0.8268.... Generator Loss: 1.7754
Epoch 2/2.... Batch 1650... Discriminator Loss: 0.8072.... Generator Loss: 2.4807
Epoch 2/2.... Batch 1660... Discriminator Loss: 0.8527.... Generator Loss: 2.2174
Epoch 2/2.... Batch 1670... Discriminator Loss: 1.1310.... Generator Loss: 1.0886
Epoch 2/2.... Batch 1680... Discriminator Loss: 0.8581.... Generator Loss: 1.9239
Epoch 2/2.... Batch 1690... Discriminator Loss: 1.6313.... Generator Loss: 0.6777
Epoch 2/2.... Batch 1700... Discriminator Loss: 0.9433.... Generator Loss: 2.0572
Epoch 2/2.... Batch 1710... Discriminator Loss: 0.9640.... Generator Loss: 1.2911
Epoch 2/2.... Batch 1720... Discriminator Loss: 0.8623.... Generator Loss: 1.5977
Epoch 2/2.... Batch 1730... Discriminator Loss: 1.1517.... Generator Loss: 1.1785
Epoch 2/2.... Batch 1740... Discriminator Loss: 0.9185.... Generator Loss: 1.7748
Epoch 2/2.... Batch 1750... Discriminator Loss: 1.5683.... Generator Loss: 0.5419
Epoch 2/2.... Batch 1760... Discriminator Loss: 0.8168.... Generator Loss: 1.6164
Epoch 2/2.... Batch 1770... Discriminator Loss: 1.0186.... Generator Loss: 1.1585
Epoch 2/2.... Batch 1780... Discriminator Loss: 0.7847.... Generator Loss: 2.6500
Epoch 2/2.... Batch 1790... Discriminator Loss: 0.8439.... Generator Loss: 1.5256
Epoch 2/2.... Batch 1800... Discriminator Loss: 1.0174.... Generator Loss: 2.1355
Epoch 2/2.... Batch 1810... Discriminator Loss: 0.9160.... Generator Loss: 1.4210
Epoch 2/2.... Batch 1820... Discriminator Loss: 0.9252.... Generator Loss: 1.9728
Epoch 2/2.... Batch 1830... Discriminator Loss: 1.6294.... Generator Loss: 0.7355
Epoch 2/2.... Batch 1840... Discriminator Loss: 0.9939.... Generator Loss: 1.2415
Epoch 2/2.... Batch 1850... Discriminator Loss: 0.7623.... Generator Loss: 1.8959
Epoch 2/2.... Batch 1860... Discriminator Loss: 1.0600.... Generator Loss: 2.6332
Epoch 2/2.... Batch 1870... Discriminator Loss: 1.3518.... Generator Loss: 0.7932

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1.... Batch 10... Discriminator Loss: 10.1380.... Generator Loss: 0.0021
Epoch 1/1.... Batch 20... Discriminator Loss: 3.4722.... Generator Loss: 0.9793
Epoch 1/1.... Batch 30... Discriminator Loss: 1.8347.... Generator Loss: 5.9237
Epoch 1/1.... Batch 40... Discriminator Loss: 3.8453.... Generator Loss: 3.5415
Epoch 1/1.... Batch 50... Discriminator Loss: 1.6537.... Generator Loss: 5.7039
Epoch 1/1.... Batch 60... Discriminator Loss: 2.5162.... Generator Loss: 7.0036
Epoch 1/1.... Batch 70... Discriminator Loss: 1.5054.... Generator Loss: 2.2901
Epoch 1/1.... Batch 80... Discriminator Loss: 1.3126.... Generator Loss: 1.7715
Epoch 1/1.... Batch 90... Discriminator Loss: 4.5499.... Generator Loss: 9.2111
Epoch 1/1.... Batch 100... Discriminator Loss: 2.2857.... Generator Loss: 2.0027
Epoch 1/1.... Batch 110... Discriminator Loss: 1.2656.... Generator Loss: 3.3693
Epoch 1/1.... Batch 120... Discriminator Loss: 0.7417.... Generator Loss: 1.9555
Epoch 1/1.... Batch 130... Discriminator Loss: 1.2803.... Generator Loss: 1.3526
Epoch 1/1.... Batch 140... Discriminator Loss: 2.1310.... Generator Loss: 0.7096
Epoch 1/1.... Batch 150... Discriminator Loss: 1.1033.... Generator Loss: 2.3003
Epoch 1/1.... Batch 160... Discriminator Loss: 1.3687.... Generator Loss: 1.8816
Epoch 1/1.... Batch 170... Discriminator Loss: 1.1679.... Generator Loss: 1.0307
Epoch 1/1.... Batch 180... Discriminator Loss: 3.9314.... Generator Loss: 3.9288
Epoch 1/1.... Batch 190... Discriminator Loss: 1.9333.... Generator Loss: 0.5285
Epoch 1/1.... Batch 200... Discriminator Loss: 1.1854.... Generator Loss: 1.3136
Epoch 1/1.... Batch 210... Discriminator Loss: 1.0870.... Generator Loss: 1.6203
Epoch 1/1.... Batch 220... Discriminator Loss: 1.6373.... Generator Loss: 1.2432
Epoch 1/1.... Batch 230... Discriminator Loss: 1.4836.... Generator Loss: 0.6658
Epoch 1/1.... Batch 240... Discriminator Loss: 0.9821.... Generator Loss: 1.3024
Epoch 1/1.... Batch 250... Discriminator Loss: 1.2955.... Generator Loss: 1.1419
Epoch 1/1.... Batch 260... Discriminator Loss: 1.1317.... Generator Loss: 1.5867
Epoch 1/1.... Batch 270... Discriminator Loss: 1.2866.... Generator Loss: 1.2357
Epoch 1/1.... Batch 280... Discriminator Loss: 2.3892.... Generator Loss: 3.1243
Epoch 1/1.... Batch 290... Discriminator Loss: 1.2243.... Generator Loss: 1.3699
Epoch 1/1.... Batch 300... Discriminator Loss: 1.5535.... Generator Loss: 0.7320
Epoch 1/1.... Batch 310... Discriminator Loss: 1.3014.... Generator Loss: 1.0861
Epoch 1/1.... Batch 320... Discriminator Loss: 1.0110.... Generator Loss: 1.4450
Epoch 1/1.... Batch 330... Discriminator Loss: 1.3013.... Generator Loss: 1.2242
Epoch 1/1.... Batch 340... Discriminator Loss: 1.0974.... Generator Loss: 1.1787
Epoch 1/1.... Batch 350... Discriminator Loss: 1.1166.... Generator Loss: 1.4262
Epoch 1/1.... Batch 360... Discriminator Loss: 1.5529.... Generator Loss: 0.8773
Epoch 1/1.... Batch 370... Discriminator Loss: 2.7873.... Generator Loss: 0.2014
Epoch 1/1.... Batch 380... Discriminator Loss: 1.4093.... Generator Loss: 0.9886
Epoch 1/1.... Batch 390... Discriminator Loss: 1.0920.... Generator Loss: 1.5867
Epoch 1/1.... Batch 400... Discriminator Loss: 0.9298.... Generator Loss: 1.3564
Epoch 1/1.... Batch 410... Discriminator Loss: 1.2493.... Generator Loss: 1.6571
Epoch 1/1.... Batch 420... Discriminator Loss: 1.2825.... Generator Loss: 0.8034
Epoch 1/1.... Batch 430... Discriminator Loss: 1.2014.... Generator Loss: 1.0045
Epoch 1/1.... Batch 440... Discriminator Loss: 1.2729.... Generator Loss: 1.2212
Epoch 1/1.... Batch 450... Discriminator Loss: 1.4238.... Generator Loss: 1.4243
Epoch 1/1.... Batch 460... Discriminator Loss: 2.6802.... Generator Loss: 2.0801
Epoch 1/1.... Batch 470... Discriminator Loss: 1.5403.... Generator Loss: 0.9683
Epoch 1/1.... Batch 480... Discriminator Loss: 1.2540.... Generator Loss: 1.0587
Epoch 1/1.... Batch 490... Discriminator Loss: 1.0457.... Generator Loss: 1.6966
Epoch 1/1.... Batch 500... Discriminator Loss: 1.3681.... Generator Loss: 0.9593
Epoch 1/1.... Batch 510... Discriminator Loss: 0.9960.... Generator Loss: 1.5169
Epoch 1/1.... Batch 520... Discriminator Loss: 1.3088.... Generator Loss: 1.4838
Epoch 1/1.... Batch 530... Discriminator Loss: 1.0503.... Generator Loss: 1.1136
Epoch 1/1.... Batch 540... Discriminator Loss: 1.2406.... Generator Loss: 0.9818
Epoch 1/1.... Batch 550... Discriminator Loss: 1.1098.... Generator Loss: 1.5126
Epoch 1/1.... Batch 560... Discriminator Loss: 0.9576.... Generator Loss: 1.4735
Epoch 1/1.... Batch 570... Discriminator Loss: 1.3281.... Generator Loss: 1.4880
Epoch 1/1.... Batch 580... Discriminator Loss: 1.1357.... Generator Loss: 1.2651
Epoch 1/1.... Batch 590... Discriminator Loss: 1.4061.... Generator Loss: 1.4283
Epoch 1/1.... Batch 600... Discriminator Loss: 1.3024.... Generator Loss: 1.4633
Epoch 1/1.... Batch 610... Discriminator Loss: 1.0752.... Generator Loss: 1.2887
Epoch 1/1.... Batch 620... Discriminator Loss: 2.0106.... Generator Loss: 3.1584
Epoch 1/1.... Batch 630... Discriminator Loss: 1.2512.... Generator Loss: 1.1645
Epoch 1/1.... Batch 640... Discriminator Loss: 1.0764.... Generator Loss: 1.0015
Epoch 1/1.... Batch 650... Discriminator Loss: 1.7007.... Generator Loss: 0.5647
Epoch 1/1.... Batch 660... Discriminator Loss: 1.4702.... Generator Loss: 0.7204
Epoch 1/1.... Batch 670... Discriminator Loss: 1.3088.... Generator Loss: 1.4433
Epoch 1/1.... Batch 680... Discriminator Loss: 1.2058.... Generator Loss: 1.5817
Epoch 1/1.... Batch 690... Discriminator Loss: 2.0338.... Generator Loss: 1.2334
Epoch 1/1.... Batch 700... Discriminator Loss: 1.1546.... Generator Loss: 1.3842
Epoch 1/1.... Batch 710... Discriminator Loss: 1.3124.... Generator Loss: 0.7916
Epoch 1/1.... Batch 720... Discriminator Loss: 1.0040.... Generator Loss: 1.8583
Epoch 1/1.... Batch 730... Discriminator Loss: 1.1975.... Generator Loss: 0.8844
Epoch 1/1.... Batch 740... Discriminator Loss: 1.3792.... Generator Loss: 1.7626
Epoch 1/1.... Batch 750... Discriminator Loss: 1.3556.... Generator Loss: 1.2503
Epoch 1/1.... Batch 760... Discriminator Loss: 0.9207.... Generator Loss: 1.4205
Epoch 1/1.... Batch 770... Discriminator Loss: 1.1128.... Generator Loss: 1.2051
Epoch 1/1.... Batch 780... Discriminator Loss: 1.1718.... Generator Loss: 1.3834
Epoch 1/1.... Batch 790... Discriminator Loss: 1.2491.... Generator Loss: 0.9933
Epoch 1/1.... Batch 800... Discriminator Loss: 1.3408.... Generator Loss: 1.6365
Epoch 1/1.... Batch 810... Discriminator Loss: 2.0187.... Generator Loss: 2.7098
Epoch 1/1.... Batch 820... Discriminator Loss: 1.1805.... Generator Loss: 0.9408
Epoch 1/1.... Batch 830... Discriminator Loss: 2.0085.... Generator Loss: 0.3222
Epoch 1/1.... Batch 840... Discriminator Loss: 1.2709.... Generator Loss: 1.3063
Epoch 1/1.... Batch 850... Discriminator Loss: 0.9842.... Generator Loss: 1.2411
Epoch 1/1.... Batch 860... Discriminator Loss: 1.3526.... Generator Loss: 1.7616
Epoch 1/1.... Batch 870... Discriminator Loss: 1.1357.... Generator Loss: 1.0185
Epoch 1/1.... Batch 880... Discriminator Loss: 1.2570.... Generator Loss: 1.0789
Epoch 1/1.... Batch 890... Discriminator Loss: 1.2966.... Generator Loss: 0.8847
Epoch 1/1.... Batch 900... Discriminator Loss: 0.9453.... Generator Loss: 1.2501
Epoch 1/1.... Batch 910... Discriminator Loss: 1.2231.... Generator Loss: 1.3681
Epoch 1/1.... Batch 920... Discriminator Loss: 1.1001.... Generator Loss: 1.0806
Epoch 1/1.... Batch 930... Discriminator Loss: 1.0872.... Generator Loss: 0.8941
Epoch 1/1.... Batch 940... Discriminator Loss: 1.1372.... Generator Loss: 1.0871
Epoch 1/1.... Batch 950... Discriminator Loss: 1.3457.... Generator Loss: 2.1531
Epoch 1/1.... Batch 960... Discriminator Loss: 1.6544.... Generator Loss: 0.5007
Epoch 1/1.... Batch 970... Discriminator Loss: 1.0728.... Generator Loss: 1.3437
Epoch 1/1.... Batch 980... Discriminator Loss: 1.3775.... Generator Loss: 0.8533
Epoch 1/1.... Batch 990... Discriminator Loss: 1.1511.... Generator Loss: 1.0735
Epoch 1/1.... Batch 1000... Discriminator Loss: 1.2548.... Generator Loss: 0.7824
Epoch 1/1.... Batch 1010... Discriminator Loss: 0.8011.... Generator Loss: 1.6456
Epoch 1/1.... Batch 1020... Discriminator Loss: 1.1613.... Generator Loss: 1.4847
Epoch 1/1.... Batch 1030... Discriminator Loss: 1.2369.... Generator Loss: 0.9230
Epoch 1/1.... Batch 1040... Discriminator Loss: 1.1779.... Generator Loss: 1.1178
Epoch 1/1.... Batch 1050... Discriminator Loss: 1.1034.... Generator Loss: 1.0293
Epoch 1/1.... Batch 1060... Discriminator Loss: 1.3680.... Generator Loss: 0.7435
Epoch 1/1.... Batch 1070... Discriminator Loss: 1.0321.... Generator Loss: 1.2033
Epoch 1/1.... Batch 1080... Discriminator Loss: 1.0307.... Generator Loss: 1.1019
Epoch 1/1.... Batch 1090... Discriminator Loss: 1.2127.... Generator Loss: 2.3979
Epoch 1/1.... Batch 1100... Discriminator Loss: 1.2475.... Generator Loss: 0.9418
Epoch 1/1.... Batch 1110... Discriminator Loss: 1.2124.... Generator Loss: 0.9610
Epoch 1/1.... Batch 1120... Discriminator Loss: 0.9304.... Generator Loss: 1.3963
Epoch 1/1.... Batch 1130... Discriminator Loss: 1.0960.... Generator Loss: 1.1943
Epoch 1/1.... Batch 1140... Discriminator Loss: 1.2744.... Generator Loss: 1.0392
Epoch 1/1.... Batch 1150... Discriminator Loss: 1.3345.... Generator Loss: 1.7946
Epoch 1/1.... Batch 1160... Discriminator Loss: 1.1918.... Generator Loss: 1.3113
Epoch 1/1.... Batch 1170... Discriminator Loss: 1.1654.... Generator Loss: 1.0666
Epoch 1/1.... Batch 1180... Discriminator Loss: 1.3873.... Generator Loss: 0.5967
Epoch 1/1.... Batch 1190... Discriminator Loss: 1.5317.... Generator Loss: 0.5743
Epoch 1/1.... Batch 1200... Discriminator Loss: 1.0568.... Generator Loss: 1.1413
Epoch 1/1.... Batch 1210... Discriminator Loss: 1.3707.... Generator Loss: 0.6751
Epoch 1/1.... Batch 1220... Discriminator Loss: 0.9029.... Generator Loss: 1.5138
Epoch 1/1.... Batch 1230... Discriminator Loss: 1.1255.... Generator Loss: 1.4286
Epoch 1/1.... Batch 1240... Discriminator Loss: 0.8558.... Generator Loss: 1.4595
Epoch 1/1.... Batch 1250... Discriminator Loss: 1.2708.... Generator Loss: 1.3984
Epoch 1/1.... Batch 1260... Discriminator Loss: 1.3565.... Generator Loss: 1.7490
Epoch 1/1.... Batch 1270... Discriminator Loss: 1.3082.... Generator Loss: 1.9442
Epoch 1/1.... Batch 1280... Discriminator Loss: 1.7525.... Generator Loss: 2.2169
Epoch 1/1.... Batch 1290... Discriminator Loss: 1.0766.... Generator Loss: 1.9620
Epoch 1/1.... Batch 1300... Discriminator Loss: 1.2845.... Generator Loss: 1.7906
Epoch 1/1.... Batch 1310... Discriminator Loss: 1.1009.... Generator Loss: 1.7346
Epoch 1/1.... Batch 1320... Discriminator Loss: 1.3880.... Generator Loss: 0.7014
Epoch 1/1.... Batch 1330... Discriminator Loss: 1.0650.... Generator Loss: 1.3873
Epoch 1/1.... Batch 1340... Discriminator Loss: 1.6317.... Generator Loss: 0.4581
Epoch 1/1.... Batch 1350... Discriminator Loss: 1.2127.... Generator Loss: 1.0428
Epoch 1/1.... Batch 1360... Discriminator Loss: 1.2006.... Generator Loss: 1.0909
Epoch 1/1.... Batch 1370... Discriminator Loss: 1.7176.... Generator Loss: 2.0383
Epoch 1/1.... Batch 1380... Discriminator Loss: 1.2440.... Generator Loss: 0.8605
Epoch 1/1.... Batch 1390... Discriminator Loss: 0.8586.... Generator Loss: 1.5025
Epoch 1/1.... Batch 1400... Discriminator Loss: 1.1366.... Generator Loss: 1.1359
Epoch 1/1.... Batch 1410... Discriminator Loss: 1.0859.... Generator Loss: 1.6656
Epoch 1/1.... Batch 1420... Discriminator Loss: 1.4477.... Generator Loss: 0.6412
Epoch 1/1.... Batch 1430... Discriminator Loss: 1.3209.... Generator Loss: 0.8642
Epoch 1/1.... Batch 1440... Discriminator Loss: 1.0955.... Generator Loss: 0.9735
Epoch 1/1.... Batch 1450... Discriminator Loss: 1.1674.... Generator Loss: 1.0995
Epoch 1/1.... Batch 1460... Discriminator Loss: 1.1481.... Generator Loss: 1.0011
Epoch 1/1.... Batch 1470... Discriminator Loss: 1.3830.... Generator Loss: 0.7722
Epoch 1/1.... Batch 1480... Discriminator Loss: 1.2682.... Generator Loss: 0.9200
Epoch 1/1.... Batch 1490... Discriminator Loss: 1.0695.... Generator Loss: 1.1451
Epoch 1/1.... Batch 1500... Discriminator Loss: 1.2685.... Generator Loss: 0.9244
Epoch 1/1.... Batch 1510... Discriminator Loss: 1.2257.... Generator Loss: 0.9634
Epoch 1/1.... Batch 1520... Discriminator Loss: 1.2745.... Generator Loss: 1.9667
Epoch 1/1.... Batch 1530... Discriminator Loss: 1.5871.... Generator Loss: 2.1913
Epoch 1/1.... Batch 1540... Discriminator Loss: 1.4125.... Generator Loss: 0.6749
Epoch 1/1.... Batch 1550... Discriminator Loss: 1.2863.... Generator Loss: 0.9338
Epoch 1/1.... Batch 1560... Discriminator Loss: 1.0810.... Generator Loss: 1.0652
Epoch 1/1.... Batch 1570... Discriminator Loss: 1.8834.... Generator Loss: 0.3555
Epoch 1/1.... Batch 1580... Discriminator Loss: 1.2327.... Generator Loss: 0.9085
Epoch 1/1.... Batch 1590... Discriminator Loss: 1.3051.... Generator Loss: 0.7580
Epoch 1/1.... Batch 1600... Discriminator Loss: 1.2226.... Generator Loss: 1.3514
Epoch 1/1.... Batch 1610... Discriminator Loss: 1.3070.... Generator Loss: 1.6460
Epoch 1/1.... Batch 1620... Discriminator Loss: 1.3567.... Generator Loss: 0.9455
Epoch 1/1.... Batch 1630... Discriminator Loss: 1.1265.... Generator Loss: 1.6208
Epoch 1/1.... Batch 1640... Discriminator Loss: 1.2546.... Generator Loss: 0.7590
Epoch 1/1.... Batch 1650... Discriminator Loss: 1.1788.... Generator Loss: 1.3040
Epoch 1/1.... Batch 1660... Discriminator Loss: 1.0916.... Generator Loss: 1.3913
Epoch 1/1.... Batch 1670... Discriminator Loss: 1.0274.... Generator Loss: 1.0168
Epoch 1/1.... Batch 1680... Discriminator Loss: 1.4113.... Generator Loss: 0.7100
Epoch 1/1.... Batch 1690... Discriminator Loss: 1.4355.... Generator Loss: 1.1648
Epoch 1/1.... Batch 1700... Discriminator Loss: 1.2669.... Generator Loss: 1.1707
Epoch 1/1.... Batch 1710... Discriminator Loss: 1.2058.... Generator Loss: 1.1380
Epoch 1/1.... Batch 1720... Discriminator Loss: 1.2775.... Generator Loss: 1.0141
Epoch 1/1.... Batch 1730... Discriminator Loss: 0.9297.... Generator Loss: 2.2164
Epoch 1/1.... Batch 1740... Discriminator Loss: 1.2261.... Generator Loss: 0.8872
Epoch 1/1.... Batch 1750... Discriminator Loss: 1.1138.... Generator Loss: 0.8891
Epoch 1/1.... Batch 1760... Discriminator Loss: 1.8384.... Generator Loss: 1.6736
Epoch 1/1.... Batch 1770... Discriminator Loss: 1.6603.... Generator Loss: 2.2423
Epoch 1/1.... Batch 1780... Discriminator Loss: 1.2493.... Generator Loss: 0.7323
Epoch 1/1.... Batch 1790... Discriminator Loss: 1.5175.... Generator Loss: 2.8937
Epoch 1/1.... Batch 1800... Discriminator Loss: 0.9986.... Generator Loss: 1.3239
Epoch 1/1.... Batch 1810... Discriminator Loss: 1.0233.... Generator Loss: 1.3216
Epoch 1/1.... Batch 1820... Discriminator Loss: 1.3896.... Generator Loss: 0.7859
Epoch 1/1.... Batch 1830... Discriminator Loss: 1.3199.... Generator Loss: 1.1907
Epoch 1/1.... Batch 1840... Discriminator Loss: 1.3824.... Generator Loss: 1.4348
Epoch 1/1.... Batch 1850... Discriminator Loss: 1.2709.... Generator Loss: 0.9852
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Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.